A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

Optimal design of supply chain networks under uncertain transient demand variations

We consider a detailed mathematical formulation for the problem of designing supply chain networks comprising multiproduct production facilities with shared production resources, warehouses, distribution centers and customer zones and operating under time varying demand uncertainty. Uncertainty is captured in terms of a number of likely scenarios possible to materialize during the lifetime of the network. The problem is formulated as a mixed-integer linear programming problem and solved to global optimality using standard branch-and-bound techniques. A case study concerned with the establishment of Europe-wide supply chain is used to illustrate the applicability and efficiency of the proposed approach. The results obtained provide a good indication of the value of having a model that takes into account the complex interactions that exist in such networks and the effect of inventory levels to the design and operation.     

Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty

The design and planning of resilient supply chains is a major challenge due to the increasing complexity of these systems that operate in a global market and therefore are more exposed to disruptions. In the present work a design and planning model that integrates demand uncertainty is applied to five supply chain structures that are submitted to different types of disruptions. Disruptions are modelled in a probabilistic manner, resulting in the incorporation of two sources of uncertainty. Eleven indicators are considered to assess the supply chains’ resilience, which comprise network design, centralization and operational indicators. The goal is to provide managers what are expected operational impacts (measured by the operational indicators) by assessing the behavior of network and centralization indicators and their known resilience behaviors from the literature. A case study of a European supply chain is used to illustrate the methodology and a discussion on the results obtained is presented in order to conclude which main characteristics a manager should consider when designing and planning resilient supply chains.     

A delayed demand supply chain: Incentives for upstream players

We study a decentralized supply chain where only delayed market demand information is available for making replenishment decisions. The impact of this delay is quantified in a serially linked two-level supply chain where each player exploits the order-up-to replenishment policy. The market demand is assumed to be a first-order autoregressive process. It is shown that the first level of the supply chain benefits from shorter time delays; however, the benefit for the second level is quite minor at best and can sometimes even be (counter-intuitively) detrimental. We conclude that the second level does not have a strong incentive to reduce the time delays in the shared market demand information.     

Inventory safety stocks of kanban control systems

Efficient production control systems should be flexible and lean. Pull control systems, especially kanban systems, lead to optimized organizational structures and order processes. This is due to their high transparency in practice. Up to now, their ability to adapt to dramatic changes in the order mix flexibility has been rated low. Existing kanban systems are based on non-adapting mathematical methods of buffer dimensioning and central organization. In this paper, the practical relevant methods of mathematical buffer dimensioning are described exemplarly. A new mathematical procedure is developed focusing on dynamic buffer stocks dimensioning including safety inventories. Its opportunities in practical employment are discussed. A realization of an electronic kanban system is described.     

The boys in the bubble: Internet entrepreneurs and stock market value

This paper expands the set of non-financial indictors of internet stock value to include that attributable to the retention of the founding entrepreneur. The failure of traditional financial value metrics to adequately explain the Internet stock boom has already provoked much research on non-financial indicators of firms value. The current paper adds to this trend of seeking “intangible” sources of Internet stocks value, by focussing on the role of the founding entrepreneur.     

Development of demand pull type control methods: The design of block action production control methods to reduce throughput time and improve supply chain synchronization

In this paper the implementation of time based manufacturing strategies and pull type control systems in an organization manufacturing electrical switchgear built to customers individual requirements are discussed in detail. The concept of block control to synchronize the production time of all internally and externally sourced components and materials is also introduced in an attempt to simplify production control methods and reduce throughput time. Savings in time and cost are described during a twenty months period of restructuring. Foundations were laid for a second objective which is an attempt to gain some linkage between throughput time reduction and resultant production costs.     

An improvement of the LP-based Aucamp model for the solution of the multi-item lot-sizing capacity-constrained problem

This paper provides a summary of the results of some studies regarding the formulation of the master production schedule. The objective of the research was to identify the optimal range of certain aggregated production planning models applied to different production situations. This paper is divided into four sections:     

Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty

We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.     

The exact fill rate in a periodic review base stock system under normally distributed demand

In this paper, we consider a periodic review order-up-to-level (or base stock) inventory control system under normally distributed demand. For such circumstances, an expression for the exact fill rate (fraction of demand satisfied without backordering) has been available in the literature, but has not been widely known, let alone used by practitioners. In this paper, we redevelop the expression and contrast our derivation with the earlier published one. The paper has two purposes. First, we hope that the reappearance of the exact result in this journal will lead to its wider adoption. Second, showing two contrasting approaches to obtaining the same result may be useful for both research and pedagogical purposes.     

缩短供应链多阶响应周期的物流模式研究

为了满足最终客户的多样化、及时化的需求,供应链的物流模式成了缩短供应链多阶响应周期的一个重要的研究领域。对比传统的物流模式,快速响应的供应链物流模式的主要改进点在制造商及其以下的节点企业的物流上。四种物流模式对于缩短供应链最终用户的响应周期有极大的意义和作用,它们是:基于制造商的直销模式、以批发商为改进点的零售支持型批发物流和区域零售支持型综合批发物流,以及以零售商为切入点的产销联盟的物流模式,并且它们各自不同的适用范围取决于供应链的运作特点和行业特征。     

A delay-in-payment contract for Pareto improvement of a supply chain with stochastic demand

We consider a supply chain consisting of a single supplier and a single retailer with stochastic customer demand, which is operated over an infinite horizon. We propose a delay-in-payment contract to coordinate the supply chain. With this contract, the supplier allows the retailer to pay partial order cost at the ordering epoch, and to pay the remaining portion after a permissible number of periods. The system is formulated as a stochastic dynamic programming problem. It is shown that there exists a base-stock policy to be optimal. Compared with the traditional wholesale-price contract, the delay-in-payment contract with appropriate parameters can achieve a Pareto improvement (i.e., the performances of both the supplier and the retailer using the delay-in-payment contract are better than those using the wholesale-price contract). Numerical studies are performed to investigate both the effectiveness of the Pareto improvement, and the impact of the major parameters of the delay-in-payment contract on the system performance.     

Recursive Behavior of Safety Stock Reduction: The Effect of Lead‐Time Uncertainty

Motivated by a recent paper on the effect of lead‐time variability reduction on safety stocks, we provide evidence of the recursive nature of safety stock changes. When lead times follow a gamma distribution we demonstrate that, for cycle service levels between .60 and .70, the reduction of lead‐time variability will first increase safety stock and then either recursively decrease safety stock or make it remain constant. We also numerically show the existence of the recursive effect. A two‐by‐two matrix is introduced to assist managers in making decisions regarding safety stock policy.     

Measuring the benefit of changing the value offering in grocery supply chains

New value-adding solutions are needed for grocery supply chains to gain a sustainable competitive advantage in a market environment characterised by increased competition. This paper discusses two case studies that identify how improvements to the value offering brought about enhancements to the performance of the total supply chain. The concept of ‘time benefit analysis’ is applied to measure the impact of the change. These approaches offer novel and unique techniques for performance measurement and value offering analysis in supply chain management.     

The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts

We quantify the bullwhip effect (which measures how the variance of replenishment orders is amplified as the orders move up the supply chain) when both random demands and random lead times are estimated using the industrially popular moving average forecasting method. We assume that the lead times constitute a sequence of independent identically distributed random variables and the correlated demands are described by a first-order autoregressive process. We obtain an expression that reveals the impact of demand and lead time forecasting on the bullwhip effect. We draw a number of conclusions on the bullwhip behaviour with respect to the demand auto-correlation and the number of past lead times and demands used in the forecasts. We find maxima and minima in the bullwhip measure as a function of the demand auto-correlation.     

Outsourcing vs. in-house production: a comparison of supply chain contracts with effort dependent demand

We analyze the effort and pricing decisions in a two facility supply chain in which one of the parties can exert costly effort to increase demand. We consider an outsourcing model in which the supplier makes the effort decision and an in-house production model in which the manufacturer decides on the effort level and we compare these two models with each other. We analyze and compare several contracts for decentralized supply chain models and we aim to find which contracts are best to use in different cases. We find the optimal contract parameters in each case and perform extensive computational testing to compare the efficiencies of these contracts. We also analyze the effect of the powers of the parties in the system and the effect of system parameters on the performances of the contracts and on the optimal values of the model variables such as price, effort and demand.     

Investigating the effect of product variants,and demand distributions on the optimal demand supply network setup

In today's marketplace, consumers are looking for product variety and low prices. In this paper, we investigate how product variation and demand distributions affect demand supply network optimality in a real-life based setting. The research methodology was based on a novel Petri net formulation, which employs reachability analysis to demand supply network optimisation. We found that in our case study localised final assembly is justified with one or two product variants, but a centralised production should be favoured with three or more product variants. We also noted the sensitivity of this result to transport cost/inventory carrying cost tradeoff.     

Impacts of demand uncertainty on retailer's dominance and manufacturer-retailer supply chain cooperation

Recent studies in marketing and distribution channels have shown that the balance of power between manufacturers and retailers is shifting. Based on this observation, we investigate a two-echelon supply chain with a manufacturer and a retailer in this paper. We first develop retailer-dominant non-cooperative game models by introducing a sensitivity of retailer's order quantity to manufacturer's wholesale price; then we analyze two cooperative scenarios, in which the Nash bargaining model is utilized to implement profit sharing between the manufacturer and the retailer. Under the assumption that the manufacturer and the retailer are risk-neutral, we find that the manufacturer and the retailer can bargain to cooperate at any level of retail-market demand uncertainty with exogenous retail price. However, the cooperation is conditional on retail-market demand uncertainty with endogenous retail price: it can be implemented if the fluctuation of retail-market demand is relatively small, and the measure of retail-market demand uncertainty does not exceed an upper bound. Theoretical and numerical analyses show that the retailer's dominance over the manufacturer increases with the increase in the sensitivity of retailer's order quantity to manufacturer's wholesale price under a limitation of retail-market demand uncertainty. Numerical analyses also show that the retailer's dominance decreases with the increase in retail-market demand uncertainty.     

An algorithm to dynamically adjust the number of Kanbans in stochastic processing times and variable demand environment

In a real-life environment, the just-in-time JIT system is subjected to various types of uncertainties such as stochastic processing times and variable demand. Since, JIT was only meant to operate in a deterministic environment, its performance is seriously affected by variations in processing times and demand. In this paper, a newly developed Kanban system is presented which uses an algorithm to dynamically and systematically manipulate the number of Kanbans in order to offset the blocking and starvation caused by the said uncertainties during a production cycle. The new system is termed a flexible Kanban system FKS . The steps of the algorithm are detailed and the effectiveness of FKS is demonstrated using an example model. For the example model, the solution procedure, results and a discussion are presented.     

需求不确定下的补偿策略理论模型

在需求不确定的前提条件下就供应商对销售商的补偿策略进行了深入研究,建立了包 含风险偏好的补偿策略理论模型. 在该理论模型的基础上,运用数值分析的手段分析了风险偏 好对供应链中参与者决策行为的影响,为供应链决策者制定合理的决策提供了科学依据. 最 后,结合我国国情,重点讨论如何在我国正确实施补偿策略.